Road Sediment Model
Student E
8/26/2004
Master of Science Capstone Industry Project Proposal
UWT Computing and Software Systems
Committee Chair: Dr. Smith Ph.D., UWT Computing and Software Systems
Committee Members: Dr. Jones Ph.D., UW Earth and Space Sciences
Abstract:
The goal of this project is to develop a computer model that will estimate the quantity of sediment that is
delivered from forest roads to streams. The Road Sediment Model (RSM) will initially be developed for
use by CompanyB, but can ultimately be adapted to other forestland owners anywhere in the world.
Previous work has shown that: (1) Fine sediments are a significant source of pollution and are harmful to
upland stream fish habitat; (2) Forest roads are one of the primary sources of sediment; (3) Sediment is
mobilized from forest roads during periods of rainfall; and (4) Vehicle traffic can greatly increase the
quantity of sediment that runs off the road surface. The success of the forest products industry in
responding to market demand while protecting the environment therefore hinges upon optimizing access to
forest resources via the road network while minimizing traffic in sensitive areas during rainfall.
Most existing approaches to modeling road sediment are limited in usability lacking because they use
averages for rainfall, traffic and road attributes over a relatively course spatial and temporal scale. The
RSM will greatly improve upon these approaches by using data for actual rainfall, actual management
activity, detailed road inventories, and geographic layers that spatially relate these entities. The RSM will
assist forest managers by allowing them to predict sediment outputs as a consequence of management
scenarios, and to compare predictions against the current estimated total of sediment delivered this year.
Forest managers can then make better management decisions under current and forecasted weather
conditions, which will reduce the cost of regulatory penalties as well as the cost to the environment.
1.
Technical Proposal:
1.1. Introduction
There are two phases to the Road Sediment Model project. The first phase consists of taking field
measurements on the client land base and estimating the relationships between rainfall, surface runoff
from the road prism, and sediment concentrations for various road types, and traffic levels. The
second phase of the project consists of creating a desktop application that can take as input geographic
data for roads, streams, harvest activities and rainfall and produce estimates for sediment delivery
quantities using the relationships that were established in the first phase. This project proposal is
primarily concerned with the second phase of the project. The following subsections provide a brief
explanation of the field work methodology as background, then go on to explain the application
requirements, and the overall component design.
1.2. Relating Rainfall, Runoff and Sediment
Scientists placed rain gauges in a representative center of each of the five areas defined to be
geologically and topographically unique (“Lithotopo Units”), in the client’s ownership on the Olympic
Peninsula of Washington State. Beginning October 2002, the team installed capacitance rods at culvert
inlets to continuously record water depth. During discrete storm events, the discharge of water from
that road and its known catchment area was recorded at the culvert outlet and related to water depth.
Scientists took water samples to determine the concentration of sediment in the water. In order to
capture the traffic variable, the team sampled active logging areas along with non-active locations. So
far, the team has recorded data for 12 road segments and approximately 40 to 60 discrete storms. Data
collection under this process will be ongoing and in 2004 shift to other parts of the ownership.
Relationships between rainfall, runoff and sediment are developed using traditional multi-variate
regression techniques. With these relationships established, it will be possible to model per-unit area
runoff rates and sediment quantities based on a given rainfall record, by road type and traffic pattern.
1.3. RSM Requirements
1.3.1. Functional Requirements
The RSM will be able to estimate past sediment delivery quantities based on known harvest
activities, recorded rainfall record, and road inventories. The RSM will be able to estimate future
sediment delivery, based on planned harvest activities, predicted typical rainfall, and predicted
road attributes (remediation work). The RSM will be able to save previously created “scenarios”
or “runs” so that comparisons can be made between various harvest and haul scenarios. The RSM
will be able to estimate haul routes based on a harvest unit origin and ultimate delivery location.
The RSM will be able to store multiple haul routes for each harvest origin. The user will be able
to edit RSM generated haul routes. The RSM will be able to derive the number of loads coming
out of each harvest unit based on recorded harvest volumes, where load data is unavailable. The
RSM will provide reporting that compares past and future estimates with budgets developed to
comply with environmental regulations. The RSM should ultimately produce estimates of the
relative accuracy of each of the model outputs giving the user an idea of how much confidence to
place in each result. The RSM should ultimately be able to run many scenarios in the background
and provide the user with an optimal solution to harvest planning and haul route selection.
1.3.2. Non-Functional Requirements
The RSM takes advantage of the client’s existing GIS, operating systems, and database platforms.
The client currently runs ESRI Arc 9.0 on Windows 2000 with a SQL Server 2000 database. and
ESRI Spatial Database Engine. The RSM will be easy to use for non-technically oriented staff
and therefore should be a standalone program rather than embedded in the ArcGIS environment.
1.4. Data
1.4.1. Harvest Activity
The RSM will access actual harvest activity data for past estimates and projected harvest activity
information for future estimates. The RSM uses the harvest unit polygon layer from the GIS to
determine the location of harvest activity. Non-spatial attributes include the date of harvest
activity, the volume of logs transported from the unit, and the number of loads. Future versions of
the RSM may include other types of forest management activity.
1.4.2. Roads
The RSM uses the road layer from the GIS. The GIS is used to build a geometric network, which
enables the use of weighted edge impedance path algorithms within the GIS software.
1.4.3. Delivery Points
The RSM will access a point feature layer that represents known locations along the road network
where roads periodically deliver sediment to the streams. The RSM will also access a related
database that consists of delivery point inventories, which include attributes about the length of
road delivery, surface type, road slope, and more.
1.4.4. Rainfall
The RSM will access historical rainfall intensities from 5 tipping bucket type rain gauges on the
client land base. The RSM will translate raw rain gauge data from time of tip to millimeters
precipitation per 5 minutes.
1.5. RSM Components and Estimate Creation
The RSM will generate sediment estimates by going through a multiple step process. The first step
will depend on a spatial and temporal extent supplied by the user. For example, I wish to estimate how
much sediment has and will be delivered on this square mile of forestland from the beginning of this
year to then end of this year.
1.5.1. Spatial Preprocessor
The three primary data inputs are harvest activity, roads and rainfall. The RSM Spatial
Preprocessor will intersect these three inputs in a similar way for both past and future estimates.
The intersection of these spatial entities will by optimized by creating a logical network that
relates delivery points, roads, streams, and rain guages. The logical network will by built by
running a spatial intersection process periodically. This step is independent of the normal
sediment estimate creation and may only be necessary when the spatial layers have changed.
1.5.2. User Interface
The RSM will be a Windows desktop application. The interface will consist of an interactive map
window, forms for text and numeric input, and a reporting window for viewing and printing the
sediment estimates in charts and tabular reports. In order to run the model, the user will select a
time interval and spatial extent, and then go to the reporting window to view the results. The user
will specify the time interval using calendar controls. The user will specify the spatial extent by
drawing an area of interest in the map, or by selecting from any polygon feature class that is
available in the Geodatabase, such as a particular basin. If the user chooses to run a future time
interval, then they must also specify a weather pattern from a selection of typical storm types.
1.5.3. Harvest Activity Manager
Based on the temporal extent supplied by the user, the Harvest Activity Manager component will
select harvest activity data that occurs during that interval.
1.5.4. Haul Route Activity Manger
Using the location of the harvest unit obtained in the previous step, and the road network, the Haul
Route Activity Manager component uses a modified weighted edge shortest path algorithm to find
the collection of road segments that represent the route from harvest unit to paved road. In some
cases there may be more than one route that is used to transport logs from the harvest unit. Haul
routes will be persisted in the RSM so that future runs that involve the same harvest unit origin
won’t need to rerun the process.
1.5.5. Delivery Point Activity Manager
Each road segment in the route may or may not intersect with one or more delivery points. For
each delivery point that intersects with haul route activity, the number of loads running over that
delivery point will be summed, and delivery point inventory records will be imported with a date
closest to the date of the haul route activity.
1.5.6. Rainfall Manager
Past rainfall intensity data is supplied by rain guages. The RSM will generate an average rainfall
record for any future time interval.
1.5.7. Delivery Point Results Manager
The Delivery Point Results component incorporates the spatial and temporal data generated from
the other components, and applies the runoff and sediment functions to derive a runoff volume and
sediment concentration for each delivery point and each 5 minute time step based on the rainfall
intensity, the road characteristics, and road use class.
1.5.8. Confidence Estimator
As an enhancement beyond the initial deliverable for client, the RSM should ultimately produce
measures of accuracy in terms of confidence levels and confidence intervals for each of the model
outputs giving the user an idea of how much confidence to place in each result.
1.5.9. Harvest and Haul Activity Optimizer
As an enhancement beyond the initial deliverable for client, the RSM should ultimately be able to
run many scenarios in the background and provide the user with an optimal solution to harvest
planning and haul route selection.
1.6. Component Diagram
Figure 1: Component design and process overview
2.
Related Work:
2.1. Physical experiments in road sediment estimation
There have been numerous studies to estimate the amount of sediment that is deposited from forest
roads, and to determine what are the primary causal relationships. We focus here on those that are
most closely related to our geographic area of interest, and surmised to be the most comprehensive
assessment of the problem.
2.1.1. Black and Luce
Black and Luce wrote several papers in 1999 and 2001 that discovered correlations between road
length, slope, base soil type, cut slope cover, road use and road maintenance on forest roads in the
Oregon Coast Range. They found that certain soil types delivered significantly more than others,
and that certain road maintenance practices caused sediment production equivalent to high log
truck traffic.
2.1.2. Megehan, Ketcheson, Monsen, Wilson, King
These authors worked together on several papers between 1991 and 2001 that studied sediment
delivery from forest roads in central Idaho. These studies identified sources of sediment,
deposition locations, cumulative volumes of sediment, and the effects of road construction and
erosion control practices.
2.1.3. Reid and Dunne
The seminal paper titled “Sediment Production from Forest Road Surfaces” by Leslie Reid and
Thomas Dunne (1984) provided much of the inspiration for the RSM, as it established the relative
significance of road characteristics, traffic, and rainfall in sediment delivery, as well as a costeffective methodology for expanding the work. This study was particularly pertinent to our model
as it took place in the same geographic area, with similar geology, climate and roads.
2.2. Computer models estimating road sediment
2.2.1. WARSEM – SEDMODL2
SEDMODL was originally developed by Boise Cascade Corporation and has been supported by
other industry and government partners. SEDMODL first attempts to identify locations of
sediment delivery by looking at intersections between roads and streams along with topography.
SEDMODL then estimates sediment delivery quantities using average annual precipitation,
geologic erosion rates, road characteristics and average road use. This model has several
deficiencies: 1) it tends to overestimate the quantity of road segments that deliver sediment, (2) it
uses average annual precipitation, and average road use rather than actual rainfall records and
management activity data at a more realistic, finer temporal scale. (3) relationships between
rainfall, runoff and sediment are not calibrated to the specific land base of the user. WARSEM is
a new name for the newest version of SEDMODL2, which consists of an Access Database
backend, an Access user interface, and various ARCINFO scripts for doing spatial operations.
2.2.2. WEPP – X-DRAIN
The Water Erosion Prediction Project was developed by USDA Agricultural research service. The
WEPP model is a physically based soil erosion model that can provide estimates of soil erosion
and sediment yield considering specific soil, climate , ground cover, and topographic conditions.
For every day being modeled, WEPP simulates vegetation, surface residue and soil water content.
For each day with precipitation, WEPP determines whether the precipitation is rain or snow, and
calculates the infiltration and runoff. If there is runoff, WEPP routes the runoff over the surface,
calculating runoff and deposition rates for at least 100 points on the hill slope. It then calculates
the average sediment yield from the hill slope. X-DRAIN is one of a series of USDA Forest
Service computer programs and uses the WEPP model specifically to estimate sediment from
forest roads and to determine the optimum number of cross drains needed to mitigate sediment
delivery. X-DRAIN has similar deficiencies to the WARSEM-SEDMODL2 package in that it
estimates sediment based on average climate and traffic conditions at a temporal scale too course
to be meaningful to day to day operational management decisions.
3.
Validation of Project:
I will validate this project using established validation and verification (V & V), processes for
validating computerized simulation models. Model validation is usually defined to mean
“substantiation that a computerized model within its domain of applicability possesses a satisfactory
range of accuracy consistent with the intended application of the model” (Schlesinger et al. 1979).
Model verification is defined as ensuring that the implementation of the program correctly captures the
logical representation of the problem entity. Validation and verification is ideally performed as a
process concurrent with model development. This process can be further broken down into these parts:
Figure 2: Validation and Verification Modeling Process
3.1. Data Validation:
Data validation is concerned with ensuring that input data is appropriate, accurate and sufficiently
available. Phase one of the project was already designed properly to ensure ample quantity and
accuracy for rainfall, sediment and road attribute data. Road inventory data is sufficiently available
and growing. All relevant geographic data is already established as being sufficiently accurate for the
problem domain. Future work in this area will involve ensuring that there is a satisfactory mapping
between the road attributes collected during the runoff and sediment experiments, and the road
attributes collected during road inventories. A reasonable mapping will be necessary to run the model
and extrapolate sedimentation for all delivery points.
3.2. Conceptual Model Validation:
Sargent (1998) says that, “conceptual model validity involves determining that the theories and
assumptions underlying the conceptual model are correct and that the model representation of the
problem entity and the model structure, logic, and mathematical and causal relationships are
“reasonable” for the intended purpose of the model.” By using previous work by Reid and Dunne
(1984), we are taking advantage of relationships between model variables established using rigorous
statistical methods. We recently met with one of the study's original authors, Leslie Reid, to discuss
the validity of applying the relationships to a geographic model and received positive feedback that in
her expert opinion, the underlying logic seemed reasonable.
3.3. Computerized Model Verification:
Sargent (1998) says that, “computerized model verification ensures that the computer programming
and implementation of the conceptual model are correct”. I will continue to use established software
engineering best practices to ensure correctness, including: object-oriented design, top-down design
and program modularity. I have designed the system thus far to separate each of the major submodels
and model functions. I will also write unit tests for each functions with any logical complexity to
ensure proper logical behavior and results.
3.4. Operational Validation:
According to Sargent (1998), says that “…operational validity is concerned with determining that the
model’s output behavior has the accuracy required for the model’s intended purpose over the domain
of its intended applicability”. The rainfall to runoff submodel created in phase one has already been
validated in previous work by Marbet (2003). Future work under phase one will develop and validate
the rainfall/runoff to sediment submodel. Overall operational validation of the RSM will be performed
by comparing model outputs to actual system behavior. System behavior will be measured using the
same methods from the original study. Predicted and actual system behavior will be compared and
evaluated using graphical comparisons, and confidence intervals will be developed for estimations.
3.5. Functional Prototype
The project can be considered complete once a prototype that fulfills all of the previously mentioned
requirements (section 1.3) has been built and successfully system tested by the client.
4.
Written Deliverables:
4.1. Requirements Documentation
The written deliverables must include the final version of the clients requirements that define what the
software product must do.
4.2. Validation and Verification Documentation
The written deliverables must include a summary and detailed evaluation of the model data validity,
conceptual model validity, computer model verification, and operational validity.
4.3. UML Data Model
The written deliverables must include the final logical and/or physical data model that represents the
back-end data storage for the RSM.
4.4. UML Object Model
The written deliverables must include the final version of the object model that represents the main
classes written to implement the components of the RSM.
4.5. User Help System
The written deliverables must include help system written to help the end-user of the RSM to run the
model.
4.6. Fully commented source code
The written deliverables must include the source code of the RSM implementation, complete with
comments for each class and method.
4.7. Code comment generated html docs
The written deliverables must include the auto-generated documentation from code comments.
5.
Oral Deliverables:
5.1. Oral Presentation
Oral presentation at UWT CSS colloquium, at conclusion of Winter quarter 2004/2005
6.
Educational Statement:
6.1. UWT Coursework:
This project will draw heavily upon lessons learned in database design and implementation since the
RSM will store all persistent data in a relational database and many of the functions will be
decomposed into relational operations. The project also relies heavily on lessons learned from
Software Engineering. This will be the student’s first major software development project and will be
done individually for a corporate client on a time and materials basis with a deadline. Proven software
engineering methodology must be employed to achieve a successful result. I will use knowledge
gained from Advanced Algorithms and Theory of Computation to assess the feasibility and efficiency
of computationally intensive operations. Data Mining techniques will be useful for development of the
two enhancement modules optimizing harvest planning and route selection.
6.2. Further research required:
A considerable portion of the effort required for implementing the RSM will be dedicated to taking the
theory learned from the TCSS program and applying it to specific technologies. Based in the nonfunctional requirements described earlier, I will have to expand on proficiency in geographic
information systems programming with Microsoft .NET and ESRI ArcObjects. The project will also
require further theoretical research in the areas of natural resource modeling and assignment of
confidence intervals to model components, forest road geomorphology and sediment transport
modeling, road network and traffic modeling, and precipitation modeling.
7.
Timeline and Milestones:
Fieldwork for the RSM began in the fall of 2002 and is ongoing. Continuous monitoring will be
performed to validate model outputs and refine model parameters over time. I began design and
implementation of the RSM in April 2004 and will continue through project completion. Delivery of
the first prototype is due December 2004. Model enhancements and future versions will continue into
2005. Major milestones can be seen in figure 3 and include requirements and design, client data
import, spatial preprocessor, run creation, run editing, user-interface, enhancements and validation &
verification. Figure 3 also indicates the approximate completion of tasks as of May 16, 2004. The
client has stipulated that all features except for the confidence estimator and the harvest and haul
optimizer be delivered by December 2004. The development of these two components will closely
coincide with Winter quarter 2004/2005.
Figure 3: Timeline and Milestones for Road Sediment Model Project
8.
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